Call of Papers for Current Volume **************** OnLine Submission of Paper

Volume & Issue no: Volume 10, Issue 7, July 2021

_____________________________________________________________________________

Title:
Satellite Image Enhancement Using Principal Component Analysis (PCA) Transformation Technique to Maximize the Signal-to-Noise ratio for Hyper-Spectral Data
Author Name:
Dahiru Mohammed Zakari , Bubakari Joda, Kabiru Abubakar Yahya
Abstract:
ABSTRACT Image measurements are made at many narrow contiguous wavelength bands, resulting in a complete spectrum for each pixel. The total bands include the: First 70 bands in the visible and near-infrared, and the second with 172 bands in the shortwave infrared region. As result, at levels 1 processing, only 198 of the bands are calibrated; radiometric values in the remaining bands arrest to 0 for most data products. The larger number of spectral bands provides the potentials for derived detailed information on the nature and properties of different surface materials on the ground, but mean difficulty in image processing and a large data redundancy due to high correlation among the adjacent bands. The principal component analysis (PCA) technique has been applied to reduce the data dimension and feature extraction from hyper-spectral data for assessing the biophysical and biochemical parameters With a covariance (or correlation) matrix calculated from the data, it is commonly believed that the eigenvalues and the corresponding eigenvectors computed from the covariance (or correlation) matrix can enhance vegetation variation information in the first PCs. Developed one transformation method called “maximum noise fraction” (MNF) transform to maximize the signal-tonoise ratio when choosing PCs with increase component numbers. Therefore, several MNFs to maximize the signal-tonoise ratio are selected for analysis for hyper-spectral data, Such as for determining end member spectra for spectral mixture analysis. Therefore, the larger number of spectral bands provides the potentials for derived detailed information on the nature and properties of different surface materials on the ground. Keywords: Hyper-Spectral Data, Enhancement, wavelength bands, Maximum Noise Fraction” (MNF)
Cite this article:
Dahiru Mohammed Zakari , Bubakari Joda, Kabiru Abubakar Yahya , " Satellite Image Enhancement Using Principal Component Analysis (PCA) Transformation Technique to Maximize the Signal-to-Noise ratio for Hyper-Spectral Data " , International Journal of Application or Innovation in Engineering & Management (IJAIEM) , Volume 10, Issue 7, July 2021 , pp. 051-061 , ISSN 2319 - 4847.
Full Text [PDF]                           Back to Current Issue